Judicial applications of artificial intelligence

187 Pages·1998·65.68 MB·English

by  Sartor, Giovanni, Branting, L Karl

JUDICIAL APPLICATIONS OF
ARTIFICIAL INTELLIGENCE
■J
Edited by
GIOVANNI SARTOR
The Queen’s University of Belfast
Northern Ireland, UK
and
KARL B RANTING
University of Wyoming
USA
Reprinted from Artificial Intelligence and Law 6(2-4) 1998
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CONTENTS
G. SARTOR and L. KARL BRANTING / Introduction: Judicial Appli­
cations of Artificial Intelligence 1
L. KARL BR ANTING, JAMES C. LESTER and CHARLES B.
CALLAWAY /Automating Judicial Document Drafting: A Discourse-
Based Approach 7
URI J. SCHILD / Criminal Sentencing and Intelligent Decision Support 47
CYRUS TATA / The Application of Judicial Intelligence and ‘Rules’ to
Systems Supporting Discretionary Judicial Decision-Making 99
HENRY PRAKKEN and GIOVANNI SARTOR / Modelling Reasoning
with Precedents in a Formal Dialogue Game 127
PHILIP LEITH / The Judge and the Computer: How Best ‘Decision
Support’? 185
MICHELE TARUFFO / Judicial Decisions and Artificial Intelligence 207
^ Artificial Intelligence and Law 6: 105-110, 1998.
105
Introduction: Judicial Applications of Artificial
Intelligence
G. SARTOR
Law Faculty, The Queen’s University of Belfast, Belfast BT7 INN, UK
L. KARL BRANTING
Department of Computer Science, University of Wyoming, Laramie, WY 82071, USA
The history of artificial intelligence (AI) has been characterized by a steady expan­
sion of the aspects of human cognition and expertise amenable to computational
models. The earliest AI applications were in formal domains, such as theorem prov­
ing, that are relatively divorced from the complexity of ordinary human experience.
Progress in natural language processing, expert systems, planning, robotics, and
qualitative reasoning has extended the range of human experience and behavior
addressed by AI. This general trend also characterizes research on AI & law.
Early research addressed primarily routine legal domains, such as administrative
procedures, that involve the straightforward application of clear-cut rules to un-
controversial facts. Recent research, however, has focused increasingly on more
complex and challenging areas of the legal process typified by judicial decision­
making. No simple rule-chaining or pattern-matching algorithm can accurately
model judicial decision-making because the judiciary has the task of producing
reasonable and acceptable solutions in exactly those cases in which the facts, the
rules, or how they fit together are controversial.
Judicial decision-making is an area of daunting complexity, where highly so­
phisticated legal expertise merges with cognitive and emotional competence. Many
of the central concepts in the judicial application of the law - such as “justice”,
“reasonable care”, and “intent” - are deeply enmeshed in the fabric of human life.
Moreover, judicial reasoning combines diverse cognitive skills, such as assessing
facts, interpreting texts, making analogies, and engaging in dialectical interactions.
Besides its complexity, judicial decision-making is also characterized by its social
importance. It is, indeed, the most characteristic moment of legal experience. Indi­
vidual cases may involve important interests and deep feelings, and their solution
impacts upon the expectations of all legal actors and shapes their understanding of
the legal system.
These features of judicial activity justify a cautious approach. The hazards of
replacing judicial discretion with a rigid computer model can hardly be overesti-
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106 G. SARTOR AND L. KARL BRANTING
mated (as stressed by Weizenbaum 1976, Gardner 1987, Berman & Hafner 1989,
among others). However, AI research projects in this field have consistently abjured
any attempt to usurp the discretionary reasoning of judges. Rather than aiming at
the impossible dream (or nightmare) of building an automatic judge, AI research
has aimed at developing practical tools to support judicial activities as well as new
analytical tools for understanding and modeling judicial decision-making.
1. Modeling judicial tasks
No form of legal reasoning seems to depend more heavily on uniquely human
abilities than the decision-making of a judge. Judicial decision-making requires
assessing the credibility of witnesses, evaluating the probative weight of evidence,
interpreting the meaning and intended effect of legal statutes and other normative
authorities and, especially in criminal cases, balancing mercy with justice. How
can AI contribute to a process that encompasses such a wide range of knowledge,
judgment, and experience?
The answer to this question, like the answer to the general AI problem of build­
ing intelligent artificial agents, is that one must begin by identifying the individual
tasks that collectively constitute the overall task of judicial problem solving. The
information-processing requirements of each of these individual tasks must then
be carefully analyzed, and computational methods identified that can satisfy these
information-processing requirements. When this analysis is complete, determining
appropriate algorithms and data structures to implement the required computational
methods is typically relatively straightforward. Finally, a conceptual presentation
of the knowledge involved in the tasks must be devised to mediate the input to, and
output from, the program and to facilitate knowledge acquisition. Often the most
valuable contribution of an AI project is the analysis of the underlying real-world
tasks and their information-processing requirements, since it is this analysis that
bridges the gap between expertise in the application domain and computer science
techniques.
Such an analysis is particularly important in developing automated systems for
as complex a process as judicial problem solving. A naive application of a given AI
algorithm, such as production rule chaining, neural nets, or decision-tree induction,
to a judicial task would be unlikely either to provide informative results or to be
accepted, were it divorced from the analysis, the nature of the task, its information­
processing requirements, or the necessary computational methods.
To face the forbidding objective of modeling judicial decision-making, AI &
law research must draw insights from many disciplines that have also studied judi­
cial decision-making, such as legal theory, legal procedure, psychology, sociology,
and organization theory. Legal theory, in particular, has traditionally dedicated the
largest part of its methodological analyses to judicial problem solving and offers
a rich palette of prescriptive and descriptive models. However, for several reasons
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JUDICIAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE 107
the contributions of these disciplines can only represent a starting point for an
AI-oriented analysis of the judicial process.
First, these disciplines do not provide the precision and the thoroughness that
is required of a computational model. In this first regard, AI & law research has
the potential to produce a new, more articulate, and rigorous representation of
the judicial practice. Such a representation, while supplying the background for
computing applications, can also provide a substantial contribution to the theory of
the judicial process and of judge-made law.
Second, the available models of judicial activities focus only on a few central
judicial activities, such as evaluating factual evidence and interpreting the law,
and disregard many ancillary judicial activities, such as producing documents and
accessing information. These ancillary tasks may in fact be the activities most
amenable to AI & law techniques.
Although the history of research in judicial applications of AI has been rela­
tively brief, AI & law has already obtained substantive results in both the directions
of research just mentioned. Contributions addressing the most central features of
judicial reasoning and judge-made law include the study of case-based reasoning.
In case-based reasoning, AI & law has provided new, powerful theoretical models
of aspects of judicial reasoning that integrate and develop the insights of legal
theory. Contributions to the analyses of ancillary activities include various projects
addressing preliminary or complementary judicial tasks (e.g., jurisdictional screen­
ing, drafting routine court documents, procedural tracking) or focusing on the
objective of helping pro se and other parties to successfully interact with the courts
(e.g., by assisting in the drafting of petitions and citations).
2. The papers in the special issue
Within this special issue we have tried to cover several different directions of cur­
rent research concerning AI and the judiciary, including descriptions of prototypes
and applications, formal analysis of the basic aspects of judicial reasoning, and
methodological analysis of judicial decision-making.
The opening paper by Karl Branting, James Lester, and Charles Callaway, en­
titled “Automating Judicial Document Drafting: A Discourse-Based Approach”,
addresses a task that is ancillary to judicial decision-making but which has a sig­
nificant impact on the workload of judicial offices: the drafting of routine ancillary
judicial documents. Branting et al. propose a model of documents that makes ex­
plicit the goals that documents are intended to achieve and the stylistic conventions
to which they must conform. This model, termed the document grammar model,
can be used to automate the construction of new documents. Branting et al. describe
a representative class of judicial documents - appellate jurisdictional show-cause
orders - illustrate how show-cause orders can be represented in terms of a docu­
ment grammar, and describe an implementation of a prototype document planning
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108 G. SARTOR AND L. KARL BRANTING
system that uses a document grammar to automate the drafting of new show-cause
orders.
The problem of the assistance to judges in the exercise of discretionary judg­
ment already addressed in contributions such as JEDA (Pethe et al. 1989) and
LawClerk (Branting 1993) is considered at length in Uri Schild’s paper “Crimi­
nal Sentencing and Intelligent Decision Support”. Schild’s contribution focuses on
decision support in criminal cases, a problem which is highly controversial in many
countries. The paper first addresses the legal and political background of criminal
sentencing and the objective of sentencing policies. Schild argues that the primary
objective of sentencing systems should not be to impose some superficial measure
of uniformity, but rather to help the judge to balance uniformity (and fairness)
with the classical objectives of punishment (retribution, deterrence, prevention and
rehabilitation), in the framework of the policy of the legislator. Sentencing systems
should not substitute for the judge, but rather provide information or alternative
suggestions for human choices. Schild provides a critical review of various com­
puter systems for sentencing support, including algorithmic, rule-based, case-based
and model-based systems, and relates these systems to different sentencing poli­
cies. Finally, he describes a prototype case-based sentencing system and compares
case-based to statistical and rule-based approaches.
The problems of support to sentencing is also addressed by Cyrus Tata in the
paper entitled “The application of Judicial Intelligence and ‘Rules’ to Systems
Supporting Discretionary Judicial Decision-Making”. Tata focuses primarily on
ways to assist the exercise of judicial discretion in penal cases, with special ref­
erence to a system intended to assist Scottish High Court Judges in the process
of criminal sentencing. After discussing various policies for sentencing, particu­
larly those reforms that are intended to produce uniformity and consistency by
restricting judicial discretion, he analyses various systems for sentencing support.
Tata focuses on systems that are intended to provide the user with information
about the range of penalties passed by the court for similar cases in the past.
For the selection of relevantly similar cases, he argues that classifications based
on doctrinal categories are inadequate. He advocates instead a “schematic holistic
representation”, a broad classification reflecting the mental schemata used by the
judges for comparing criminal histories.
The article of Henry Prakken and Giovanni Sartor addresses the representation
of precedents and the application of case law. It builds upon previous research
(particularly the HYPO project by Kevin Ashley and Edwina Rissland) to provide
a general framework for legal precedent that integrates results from case based
reasoning and formal dialectics. Prakken and Sartor view judicial opinions as artic­
ulate (multi-argument, multi-step and multi-level) dialectical structures and strive
to preserve this articulation in their formal representation. They therefore represent
each precedent as a collection of arguments, any portion of which can be cited
in new disputes according to a dialectical protocol. The protocol provides heuris­
tics that replicate the basic non-deductive moves of case based reasoning (such as
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JUDICIAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE 109
analogizing and distinguishing a precedent). Finally, the paper presents a method
for assessing conflicts between precedents that generalizes HYPO’s on-pointness
ordering and allows it to be combined with other choice criteria. The model de­
veloped by Prakken and Sartor is intended to provide both a formalized theory of
certain aspects of judicial law-making and a formal foundations for certain aspects
of computer programs for case-based reasoning.
The contribution by Philip Leith, entitled “The Judge and the Computer: How
Best ‘Decision Support’ ”, both complements and contrasts with the views devel­
oped in the other papers of this special issue. While those papers adopt a cautious,
but positive and constructive, attitude towards AI applications in the judiciary do­
main, Leith stresses the limits of some AI technologies. He first discusses some
earlier expert systems (such as Prospector, Dendral and Mycin), arguing that they
were not accepted by the intended users because they were not based upon a
recognition of users’ needs, function, and role. He further argues that AI & law
applications are bound to make the same mistake and therefore to being rejected
by their users. He stresses that discretion is valuable to the judges, so that every
attempt to reduce it through computers will be resisted by the judiciary. Moreover,
his view is that formalization (such as is required in the construction of a consis­
tent rule-based knowledge-base, sufficient to solve future cases) is fundamentally
incompatible with the judicial function in the most interesting areas of law. This
thesis is argued for from a rule-sceptical point of view, according to which legal
rules are only created by agents of the legal process (judges and barrister), sentenc­
ing is a social process characterized by the negotiation between those agents, in the
framework of social pressures and expectations, and under resource constraints.
The article by Michele Taruffo, which concludes this special issue, provides a
link between current jurisprudential research on judicial reasoning and of AI &
law. Taruffo views each case as a cluster of linked legal and factual issues, each
of which can be given different solutions. From this perspective the situation of
the court at the moment of its final decision is characterized by the existence of
several possible projects of decision and by the obligation of the court to choose
one of them as the best possible decision in that context. Taruffo observes that the
idea that the judge’s reasoning could or should be completely reduced to simple
logical models is untenable. However, he observes that the failure of simple logical
models does not impair, but rather makes more valuable, AI & law research, which
has succeeded in going far beyond such simple models and which offers a wide and
growing inventory of analytical tools, some of which seem to fit judicial reasoning.
He focuses then on the automation of judicial discretion, distinguishing different
forms of discretion and discussing the contribution of AI to the treatment of each
of them. Finally, he addresses the problems of the logic of judicial justification,
where he points to the advantages that legal theory can derive from computational
dialectics.
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110 G. SARTOR AND L. KARL BRANTING
3. Conclusion
The short history of AI for the judiciary, while emphasizing the difficult chal­
lenges that AI & law has to face, also displays the variety of solutions which
our discipline has so far provided. As a reply to those challenges, AI & law has
developed increasingly sophisticated models and techniques that address many of
the concerns of the critics of earlier AI models, including case-based reasoning,
formal dialectics, theory construction, neural networks, formal argumentation and
negotiation, intelligent document assembly, and tools for supporting discretional
decision-making. These achievements are useful and important for the judiciary,
because they provide a deeper and clearer understanding of some aspects of judicial
problem-solving, and an effective support to the judges and their collaborators.
Judges, squeezed between tightened budgets and increasing demands for jus­
tice, are desperately trying to maintain the quality of their decision-making process
while coping with time and resource limitations. Flexible AI tools for decision-
support may promote a sufficient degree of uniformity and efficiency in judicial
practice, while supporting a rational exercise of judicial discretion (and so possi­
bly help to prevent, for example, the draconian rigidity of compulsory sentencing
guidelines). In the same way, AI may help to reconcile flexibility, efficiency and ac­
curacy in complementary tasks, such as the drafting of various judicial documents.
In conclusion, we believe that the judiciary is in the early stages of a trans­
formation in which AI technology will make the judicial process faster, cheaper,
and more predictable without compromising the integrity of judges’ discretionary
reasoning. We hope that the papers in this volume exemplify some of the directions
that this transformation will take.
References
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CACM 32(8): 928-938.
Branting, K. (1993) ‘An Issue-Oriented Approach to Judicial Document Assembly’, In Proceedings
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ACM Press.
Gardner, A. (1987) An Artificial Intelligence Approach to Legal Reasoning. MIT Press: Cambridge
(MA).
Pethe, V.P., Rippey, C.P., and Kale, L.V. (1989) ‘A Specialized Expert System for Judicial Decision
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